Gu, Lifang and Li, Jiuyong and He, Hongxing and Williams, Graham and Hawkins, Simon and Kelman, Chris (2003) Association rule discovery with unbalanced class distributions. In: 16th Australian Conference on Artificial Intelligence (AI 2003), 3-5 Dec 2003, Perth, Australia.
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Abstract
There are many methods for finding association rules in very large data. However it is well known that most general association rule discovery methods find too many rules, many of which are uninteresting rules. Furthermore, the performances of many such algorithms deteriorate when the minimum support is low. They fail to find many interesting rules even when support is low, particularly in the case of significantly unbalanced classes. In this paper we present an algorithm which finds association rules based on a set of new interestingness criteria. The algorithm is applied to a real-world health data set and successfully identifies groups of patients with high risk of adverse reaction to certain drugs. A statistically guided method of selecting appropriate features has also been developed. Initial results have shown that the proposed algorithm can find interesting patterns from data sets with unbalanced class distributions without performance loss.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
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| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Copyright Springer-Verlag 2003. Permanent restricted access to published version in accordance with the copyright policy of the publisher. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013) |
| Date Deposited: | 30 Nov 2007 11:55 |
| Last Modified: | 25 Aug 2014 01:47 |
| Uncontrolled Keywords: | knowledge discovery; data mining; association rules; record linkage; administrative data; adverse drug reaction |
| Fields of Research (2008): | 01 Mathematical Sciences > 0104 Statistics > 010405 Statistical Theory 08 Information and Computing Sciences > 0806 Information Systems > 080607 Information Engineering and Theory 08 Information and Computing Sciences > 0806 Information Systems > 080608 Information Systems Development Methodologies |
| Fields of Research (2020): | 49 MATHEMATICAL SCIENCES > 4905 Statistics > 490509 Statistical theory 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460912 Knowledge and information management 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460905 Information systems development methodologies and practice |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences |
| Identification Number or DOI: | https://doi.org/10.1007/978-3-540-24581-0_19 |
| URI: | http://eprints.usq.edu.au/id/eprint/11429 |
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